Reliability engineering encompasses the theoretical and practical tools by which the probability and capability of parts, components, equipment, products, and systems to perform their required functions can be specified, predicted, tested, demonstrated, installed, and initialized. Accurate reliability prediction can be used to identify and allocate resources for the implementation of changes, which can increase the reliability of a manufacturing system.
Reliability engineering tools which enable the accurate prediction of the reliability of parts, components, and systems can provide a company with a significant competitive advantage. For example, accurate prediction of the reliability of a proposed or existing manufacturing or production line can decrease costs, increase speed to market of new products, and provide more predictable project outcomes. Thus, manufacturing costs and capacity can be better understood and controlled with more accurate prediction methods, resulting in less market upset particularly during the early phases of a new product introduction.
Historically, reliability analyses and simulations have depended on methods, which determine the reliability of a machine section independent of the other machine sections. However, these analyses do not necessarily indicate with specificity the areas of a system to focus on with respect to maintenance, or to create a business impact. For example, one section of a system may experience about the same amount of failures as another section of the same system. However, the result of optimizing the one section over the other section may be indeed very different. This is because one subsystem may have an effect on the performance of another subsystem, even if indirectly connected. In addition, different events such as splices can occur which affect the number of times a machine section or system experiences down time. Current simulation software does not take into account how the inefficiencies of one section of the system can affect another section of the system.
As such, there is a need for simulation methods which can more accurately predict the reliability of the system based on interactions between machine sections and events such as splices. Further, there is a need to develop a dynamic model that can take into account the effect that system events have on all sections of a system, not just the section where the system event occurred. In addition, there is a need for simulation methods, which indicate what sections of the system will provide the most benefit in return for optimization or maintenance efforts.